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neural network

Deep Studying with Label Differential Privateness - Channel969


Over the past a number of years, there was an elevated give attention to growing differential privateness (DP) machine studying (ML) algorithms. DP has been the idea of a number of sensible deployments in business -- and has even been employed by the U.S. Census -- as a result of it allows the understanding of system and algorithm privateness ensures. The underlying assumption of DP is that altering a single person's contribution to an algorithm mustn't considerably change its output distribution. In the usual supervised studying setting, a mannequin is educated to make a prediction of the label for every enter given a coaching set of instance pairs {[input1,label1], …, [inputn, labeln]}. Within the case of deep studying, earlier work launched a DP coaching framework, DP-SGD, that was built-in into TensorFlow and PyTorch.

Disease Classification using Medical MNIST


The objective of this study is to classify medical images using the Convolutional Neural Network(CNN) Model. Here, I trained a CNN model with a well-processed dataset of medical images. This model can be used to classify medical images based on categories provided as per the training dataset. This dataset was developed in 2017 by Arturo Polanco Lozano. It is also known as the MedNIST dataset for radiology and medical imaging. For the preparation of this dataset, images have been gathered from several datasets, namely, TCIA, the RSNA Bone Age Challange, and the NIH Chest X-ray dataset.

New deep learning technique paves path to pizza-making robots


This article is part of our coverage of the latest in AI research. For humans, working with deformable objects is not significantly more difficult than handling rigid objects. We learn naturally to shape them, fold them, and manipulate them in different ways and still recognize them. But for robots and artificial intelligence systems, manipulating deformable objects present a huge challenge. Consider the series of steps that a robot must take to shape a ball of dough into pizza crusts.

DeepMind Researchers Develop A Machine Learning Technique For Accurate Sampling And Free-Energy Estimate Of Solid Materials Using Normalizing Flows


A significant challenge of computational statistical mechanics is the accurate estimation of equilibrium parameters of a thermodynamic system. For decades, the methods of choice for sampling such systems at large have been molecular dynamics (MD) and hybrid Monte Carlo. Strategies for sampling probability distributions have increased, and most try leveraging normalizing flows. Normalizing Flows are a technique for creating complicated distributions that involve changing a probability density through a sequence of invertible mappings. These are desirable because of 2 characteristics: first, they can create independent samples rapidly and in parallel, and second, they can offer the precise probability density of their creation method.

AI is changing the way people relate to other beings


Interspecies was once a technical term used in science to describe how one species got along with another. Now it is a word of more consequence: it evokes the new connections between humans and non-humans that are being made possible by technology. Whether it is satellite footage tracking geese at continental scale, or a smartphone video of squirrels in a park, people are seeing the 8.7m other species on the planet in new lights. In "Ways of Being", James Bridle, a British artist and technology writer, explores what this means for understanding the many non-human intelligences on Earth. Your browser does not support the audio element.

Self-Supervised Learning and Its Applications - neptune.ai


In the past decade, the research and development in AI have skyrocketed, especially after the results of the ImageNet competition in 2012. The focus was largely on supervised learning methods that require huge amounts of labeled data to train systems for specific use cases. In this article, we will explore Self Supervised Learning (SSL) – a hot research topic in a machine learning community. Self-supervised learning (SSL) is an evolving machine learning technique poised to solve the challenges posed by the over-dependence of labeled data. For many years, building intelligent systems using machine learning methods has been largely dependent on good quality labeled data. Consequently, the cost of high-quality annotated data is a major bottleneck in the overall training process.

Neuromorphic chips more energy efficient for deep learning


Neuromorphic chips have been endorsed in research showing that they are much more energy efficient at operating large deep learning networks than non-neuromorphic hardware. This may become important as AI adoption increases. The study was carried out by the Institute of Theoretical Computer Science at the Graz University of Technology (TU Graz) in Austria using Intel's Loihi 2 silicon, a second-generation experimental neuromorphic chip announced by Intel Labs last year that has about a million artificial neurons. Their research paper, "A Long Short-Term Memory for AI Applications in Spike-based Neuromorphic Hardware," published in Nature Machine Intelligence, claims that the Intel chips are up to 16 times more energy efficient in deep learning tasks than performing the same task on non-neuromorphic hardware. The hardware tested consisted of 32 Loihi chips.

Microsoft expands its AI partnership with Meta


Microsoft and Meta are extending their ongoing AI partnership, with Meta selecting Azure as "a strategic cloud provider" to accelerate its own AI research and development. Microsoft officials shared more details about the latest on the Microsoft-Meta partnership on Day 2 of the Microsoft Build 2022 developers conference. Microsoft and Meta -- back when it was still known as Facebook -- announced the ONNX (Open Neural Network Exchange) format in 2017 in the name of enabling developers to move deep-learning models between different AI frameworks. Microsoft open sourced the ONNX Runtime, which is the inference engine for models in the ONNX format, in 2018. Today, Meta officials said they'll be using Azure to accelerate research and development across the Meta AI group.

Early sound exposure in the womb shapes the auditory system


Inside the womb, fetuses can begin to hear some sounds around 20 weeks of gestation. However, the input they are exposed to is limited to low-frequency sounds because of the muffling effect of the amniotic fluid and surrounding tissues. A new MIT-led study suggests that this degraded sensory input is beneficial, and perhaps necessary, for auditory development. Using simple computer models of the human auditory processing, the researchers showed that initially limiting input to low-frequency sounds as the models learned to perform certain tasks actually improved their performance. Along with an earlier study from the same team, which showed that early exposure to blurry faces improves computer models' subsequent generalization ability to recognize faces, the findings suggest that receiving low-quality sensory input may be key to some aspects of brain development.

Is Artificial Intelligence Made in Humanity's Image? Lessons for an AI Military Education - War on the Rocks


Artificial intelligence is not like us. For all of AI's diverse applications, human intelligence is not at risk of losing its most distinctive characteristics to its artificial creations. Yet, when AI applications are brought to bear on matters of national security, they are often subjected to an anthropomorphizing tendency that inappropriately associates human intellectual abilities with AI-enabled machines. A rigorous AI military education should recognize that this anthropomorphizing is irrational and problematic, reflecting a poor understanding of both human and artificial intelligence. The most effective way to mitigate this anthropomorphic bias is through engagement with the study of human cognition -- cognitive science.